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Copy pathXspecT_mini.py
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XspecT_mini.py
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import search_filter
import os
from Bio import SeqIO, SeqRecord, Seq
from Bio.Seq import Seq
import Classifier
from OXA_Table import OXATable
import warnings
from copy import deepcopy
import time
import csv
import pickle
import matplotlib.pyplot as plt
import statistics
import random
from numpy import array
from numpy import sum
from collections import Counter
import psutil
import Bootstrap as bs
import sys
warnings.filterwarnings("ignore")
def xspecT_mini(file_path, XspecT, ClAssT, oxa, file_format, read_amount, csv_table, metagenome):
"""performs a BF-lookup for a set of genomes for testing purpose"""
itemlist = ["albensis", "apis", "baretiae", "baumannii", "baylyi", "beijerinckii", "bereziniae",
"bohemicus", "boissieri", "bouvetii", "brisouii", "calcoaceticus",
"celticus", "chengduensis", "chinensis", "colistiniresistens", "courvalinii", "cumulans",
"defluvii", "dispersus", "equi", "gandensis", "gerneri", "gs06", "gs16", "guerrae",
"guillouiae", "gyllenbergii", "haemolyticus", "halotolerans", "harbinensis", "idrijaensis", "indicus",
"johnsonii", "junii", "kanungonis", "kookii", "kyonggiensis", "lactucae", "lanii", "larvae",
"lwoffii", "marinus", "modestus", "nectaris", "nosocomialis", "oleivorans", "parvus",
"piscicola", "pittii", "pollinis", "populi", "portensis", "pseudolwoffii", "pullicarnis",
"pragensis", "proteolyticus", "puyangensis",
"qingfengensis", "radioresistens", "rathckeae", "rongchengensis", "rudis", "schindleri", "seifertii",
"seohaensis", "shaoyimingii", "sichuanensis", "soli", "stercoris", "tandoii", "terrae",
"terrestris", "tianfuensis", "tjernbergiae", "towneri", "ursingii", "variabilis", "venetianus",
"vivanii", "wanghuae", "wuhouensis", "sp."]
print("Preparing Bloomfilter...")
start = time.time()
if XspecT:
BF = search_filter.pre_processing()
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
print(f"Aktueller Speicherverbrauch mit den Spezies BF: {memory_info.rss / 1024 / 1024:.2f} MB")
# BF_1 = search_filter.pre_processing_prefilter()
BF_1_1 = search_filter.pre_processing_prefilter2()
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
print(f"Aktueller Speicherverbrauch mit dem Master BF: {memory_info.rss / 1024 / 1024:.2f} MB")
if ClAssT:
BF_2 = search_filter.pre_processing_ClAssT()
if oxa:
BF_3 = search_filter.pre_processing_oxa()
#if BioMonitoring:
#BF = search_filter.pre_processing_Culicidae_species()
#BF_1_1 = search_filter.pre_processing_prefilter_Culicidae()
end = time.time()
needed = round(end - start, 2)
print("Time needed for preprocessing: ", needed)
try:
files = sorted(os.listdir(file_path))
except FileNotFoundError:
print("Error: Invalid filepath!")
quit()
if file_format == "fna" or file_format == "fasta" or file_format == "fa":
for i in range(len(files) - 1, -1, -1):
if 'fna' in files[i] or 'fasta' in files[i]:
continue
else:
del files[i]
elif file_format == "fastq" or file_format == "fq":
for i in range(len(files) - 1, -1, -1):
if 'fastq' in files[i] or 'fq' in files[i]:
continue
else:
del files[i]
if len(files) == 0:
print("Error: No " + str(file_format) + " files in directory!")
quit()
paths = files[:]
file_path2 = file_path[:]
for i in range(len(file_path2)):
if file_path2[i] == "\\":
list_temp = list(file_path2)
list_temp[i] = '/'
file_path2 = ''.join(list_temp)
start = time.time()
for i in range(len(files)):
paths[i] = file_path2 + "/" + paths[i]
# Testing purpose delete later
# extracts the GCF-Number
# files_split = []
# for i in range(len(files)):
# files_split.append(files[i].split("_"))
# try:
# files_split[i] = files_split[i][0] + "_" + files_split[i][1]
# except:
# files_split[i] = files[i].split('.')[-2]
# GCF_taxon = []
# for i in range(len(files)):
# with open(paths[i]) as file:
# head = file.readline()
# head = head.split()
# if head[2] == "sp.":
# GCF_taxon.append("none")
# else:
# GCF_taxon.append(head[2])
# if head[2] != "baumannii":
# del files[i]
# del paths[i]
# excelv3 = []
# for i in range(len(files)):
# excelv3.append(files_split[i] + "," + GCF_taxon[i])
# for i in range(0, len(excelv3)):
# excelv3[i] = [excelv3[i]]
# with open(r'Results/XspecT_mini_csv/Test.csv', 'w', newline='') as file:
# writer = csv.writer(file)
# writer.writerows(excelv3)
if XspecT:
predictions, scores = xspecT(
BF, BF_1_1, files, paths, file_format, read_amount, metagenome)
if ClAssT:
predictions_ClAssT, scores_ClAssT = clAssT(
BF_2, files, paths, file_format, read_amount)
if oxa:
scores_oxa, scores_oxa_ind = blaOXA(BF_3, files, paths, file_format, read_amount)
#if BioMonitoring:
#predictions, scores = xspecT(
#BF, BF_1_1, files, paths, file_format, read_amount, metagenome, BioMonitoring)
print("Preparing results...")
print("")
end = time.time()
needed = round(end - start, 2)
print("Time needed: ", needed)
print("")
header_filename = "Filename"
spaces = []
space = " "
underscore = "________"
name_max = len(max(itemlist, key=len))
if XspecT:
for i in range(len(predictions)):
while len(predictions[i]) < name_max:
predictions[i] += " "
file_max = len(max(files, key=len))
while len(header_filename) < file_max:
header_filename += " "
underscore += "_"
for j in range(len(files)):
for i in range(len(header_filename)-len(files[j])):
space += " "
spaces.append(space)
space = " "
excel = []
# formatting
if ClAssT:
for i in range(len(predictions_ClAssT)):
if predictions_ClAssT[i] != "none" and predictions_ClAssT[i] != "None":
predictions_ClAssT[i] += " "
if XspecT and ClAssT:
for i in range(len(scores_ClAssT)):
if scores[i] == "1.0":
scores[i] += " "
if XspecT and ClAssT and oxa:
excelv2 = []
print(scores_oxa)
print(scores_oxa_ind)
for i in range(len(files)):
if scores_oxa == ["None"]:
excel.append(files[i] + spaces[i] + predictions[i] + " " + scores[i] + " " +
predictions_ClAssT[i] + " " + scores_ClAssT[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
else:
excel.append(files[i] + spaces[i] + predictions[i] + " " + scores[i] + " " +
predictions_ClAssT[i] + " " + scores_ClAssT[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + predictions[i] + "," + scores[i] +
predictions_ClAssT[i] + "," + scores_ClAssT[i] + "," + str(scores_oxa[i]))
print(header_filename + " Species Score Sub-Type Score blaOXA-Family blaOXA-Gene Score")
print(underscore + "___________________________________________________________________________________________________________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
elif XspecT and not ClAssT and not oxa:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] +
predictions[i] + " " + scores[i])
excelv2.append(files[i] + "," + predictions[i] + "," + scores[i])
print(header_filename + " Species Score")
print(underscore + "_________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results_XspecT.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
elif ClAssT and not XspecT and not oxa:
excelv2 = []
for i in range(len(files)):
excel.append(
files[i] + spaces[i] + predictions_ClAssT[i] + " " + scores_ClAssT[i])
excelv2.append(
files[i] + "," + predictions_ClAssT[i] + "," + scores_ClAssT[i])
print(header_filename + " Sub-Type Score")
print(underscore + "________________________________")
for i in excel:
print(i)
print("")
print("")
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results_ClAssT.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
elif oxa and not ClAssT and not XspecT:
excelv2 = []
for i in range(len(files)):
if scores_oxa == ["None"]:
excel.append(files[i] + spaces[i] + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
else:
excel.append(files[i] + spaces[i] + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + str(scores_oxa[i]))
print(header_filename + " blaOXA-Family blaOXA-Gene Score")
print(underscore + "_______________________________________________________________________")
for i in excel:
print(i)
print("")
print("")
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results_Oxa.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
elif XspecT and ClAssT and not oxa:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] + predictions[i] + " " + scores[i] +
" " + predictions_ClAssT[i] + " " + scores_ClAssT[i])
excelv2.append(files[i] + "," + predictions[i] + "," + scores[i] +
"," + predictions_ClAssT[i] + "," + scores_ClAssT[i])
print(header_filename +
" Species Score Sub-Type Score")
print(underscore + "________________________________________________________________________")
for i in excel:
print(i)
print("")
print("")
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
elif XspecT and oxa and not ClAssT:
excelv2 = []
for i in range(len(files)):
if scores_oxa == ["None"]:
excel.append(files[i] + spaces[i] + predictions[i] +
" " + scores[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
else:
excel.append(files[i] + spaces[i] + predictions[i] +
" " + scores[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + predictions[i] +
"," + scores[i] + str(scores_oxa[i]))
print(header_filename +
" Species Score blaOXA-Family blaOXA-Gene Score")
print(underscore + "_______________________________________________________________________________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
elif ClAssT and oxa and not XspecT:
excelv2 = []
for i in range(len(files)):
if scores_oxa == ["None"]:
excel.append(files[i] + spaces[i] + predictions_ClAssT[i] +
" " + scores_ClAssT[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
else:
excel.append(files[i] + spaces[i] + predictions_ClAssT[i] +
" " + scores_ClAssT[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + predictions_ClAssT[i] +
"," + scores_ClAssT[i] + "," + str(scores_oxa[i]))
print(header_filename +
" Sub-Type Score blaOXA-Family blaOXA-Gene Score")
print(underscore + "______________________________________________________________________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
def xspecT(BF, BF_1_1, files, paths, file_format, read_amount, metagenome):
"""performs a BF-lookup for a set of genomes for testing purpose"""
print("Starting taxonomic assignment on species-level...")
predictions = []
scores = []
counterx = 0
contig_header = []
contig_seq = []
for i in range(len(files)):
if i == int(len(files)/6) or i == int(len(files)/3) or i == int(len(files)/2) or i == int(len(files)/1.5) or i == int(len(files)/1.2):
print("...")
BF.number_of_kmeres = 0
BF.hits_per_filter = [0] * BF.clonetypes
BF_1_1.number_of_kmeres = 0
BF_1_1.hits_per_filter = [0]
if file_format == "fasta" or file_format == "fna" or file_format =="fa":
if metagenome:
contigs = []
contigs_classified = {}
for sequence in SeqIO.parse(paths[i], "fasta"):
contigs = []
contigs_kmers = []
BF_1_1.kmer_hits_single = []
BF_1_1.number_of_kmeres = 0
BF_1_1.hits_per_filter = [0] * BF.clonetypes
# Taking sum of list as reference, if sum has not increased after testing those 3 kmeres,
# then the contigs won't be tested further
hit_sum = sum(BF_1_1.hits_per_filter)
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
sample_size = int(len(str(sequence.seq)) ** 0.5)
threshold_contig = sample_size * 0.7
for i in range(0, len(str(sequence.seq)) - BF_1_1.k, sample_size):
if "N" not in str(sequence.seq[i: i + BF_1_1.k]):
BF_1_1.lookup(str(sequence.seq[i: i + BF_1_1.k]).upper())
# needs at least 70% hits to continue with the contig
counter = 0
#print((sum(BF_1_1.hits_per_filter) - hit_sum), threshold_contig)
if (sum(BF_1_1.hits_per_filter) - hit_sum) > threshold_contig:
#print("added")
for j in range(len(str(sequence.seq)) - BF_1_1.k):
if "N" not in str(sequence.seq[j: j + BF_1_1.k]):
contigs_kmers.append(str(sequence.seq[j: j + BF_1_1.k]).upper())
counter += 1
# how many kmers? to use
if counter >= 5000:
break
# contigs_kmers.append(str(reverse_sequence[j: j + BF_1_1.k]))
contigs.append(contigs_kmers)
BF_1_1.hits_per_filter = hits_per_filter_copy
else:
# resetting hit counter
BF_1_1.hits_per_filter = hits_per_filter_copy
continue
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
#print(f"Aktueller Speicherverbrauch 1: {memory_info.rss / 1024 / 1024:.2f} MB")
contigs_filtered = []
counter = 0
# Since we classify individual contigs now, the var contigs only contains one item which makes those loops unneccesary
for i in range(len(contigs)):
threshold = 0
for j in range(len(contigs[i])):
BF_1_1.number_of_kmeres += 1
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
BF_1_1.lookup(contigs[i][j])
if hits_per_filter_copy != BF_1_1.hits_per_filter:
threshold += 1
# parameter value needs to be determined
if threshold >= (0.7*len(contigs[i])):
contigs_filtered += contigs[i]
counter += len(contigs[i])
if counter >= 5000:
break
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
#print(f"Aktueller Speicherverbrauch 2: {memory_info.rss / 1024 / 1024:.2f} MB")
# since we do indv. contig classifications we need to reset the BF vars
BF.kmer_hits_single = []
BF.number_of_kmeres = 0
BF.hits_per_filter = [0] * BF.clonetypes
for kmer in contigs_filtered:
BF.number_of_kmeres += 1
kmer_reversed = str(Seq(kmer).reverse_complement())
if kmer > kmer_reversed:
BF.lookup(kmer)
else:
BF.lookup(kmer_reversed)
score = BF.get_score()
names = []
BioMonitoring = False
if BioMonitoring:
with open(r'filter/FilterCulicidaeSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
else:
with open(r'filter/FilterSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
score_edit = [str(x) for x in score]
score_edit = ",".join(score_edit)
# making prediction
index_result = max(range(len(score)), key=score.__getitem__)
prediction = names[index_result]
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
#print(f"Aktueller Speicherverbrauch 3: {memory_info.rss / 1024 / 1024:.2f} MB")
# skip ambiguous contigs
if max(score) == sorted(score)[-2]:
continue
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
#print(f"Aktueller Speicherverbrauch 4: {memory_info.rss / 1024 / 1024:.2f} MB")
# bootstrapping
bootstrap_n = 100
samples = bs.bootstrap(BF.kmer_hits_single, BF.number_of_kmeres, bootstrap_n)
sample_scores = bs.bootstrap_scores(samples, BF.number_of_kmeres, BF.clonetypes)
bootstrap_score = 0
bootstrap_predictions = []
for i in range(len(sample_scores)):
# skip ambiguous contigs (species with same score)
if max(sample_scores[i]) != sorted(sample_scores[i])[-2]:
bootstrap_predictions.append(names[max(range(len(sample_scores[i])), key=sample_scores[i].__getitem__)])
if max(range(len(sample_scores[i])), key=sample_scores[i].__getitem__) == index_result:
bootstrap_score += 1
else:
continue
bootstrap_score = bootstrap_score/bootstrap_n
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
#print(f"Aktueller Speicherverbrauch 5: {memory_info.rss / 1024 / 1024:.2f} MB")
# change this var to the species you want your contigs saved from
save_contigs = "none"
# saving predictions for the Bio-Monitoring
if BioMonitoring:
predictions.append(prediction)
scores.append(str(max(score)))
else:
if ("A." + prediction) not in contigs_classified:
contigs_classified["A." + prediction] = [[max(score)], 1, [len(str(sequence.seq))], sorted(score)[-2]/max(score), [bootstrap_score], contigs_filtered, None]
if prediction == save_contigs:
contig_header += [sequence.description]
contig_seq += [str(sequence.seq)]
else:
contigs_classified["A." + prediction][0] += [max(score)]
contigs_classified["A." + prediction][1] += 1
contigs_classified["A." + prediction][2] += [len(str(sequence.seq))]
contigs_classified["A." + prediction][3] += sorted(score)[-2]/max(score)
contigs_classified["A." + prediction][4] += [bootstrap_score]
contigs_classified["A." + prediction][5] += contigs_filtered
if prediction == save_contigs:
contig_header += [sequence.description]
contig_seq += [str(sequence.seq)]
#scores.append(str(max(score)))
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
#print(f"Aktueller Speicherverbrauch 6: {memory_info.rss / 1024 / 1024:.2f} MB")
else:
for sequence in SeqIO.parse(paths[i], "fasta"):
for j in range(0, len(sequence.seq) - BF.k, 500):
BF.number_of_kmeres += 1
kmer = str(sequence.seq[j : j + BF.k])
kmer_reversed = str(Seq(kmer).reverse_complement())
if kmer > kmer_reversed:
BF.lookup(kmer)
else:
BF.lookup(kmer_reversed)
score = BF.get_score()
#print("Scores: ", score)
if metagenome:
#for prediction in contigs_classified:
# kmers = contigs_classified[prediction][5]
# Strip "A."
# prediction = prediction[2:]
# kmer mapping to genome, start by loading the kmer_dict in
# path_pos = "filter\kmer_positions\Acinetobacter\\" + prediction + "_positions.txt"
# delete later
# path_posv2 = "filter\kmer_positions\Acinetobacter\\" + prediction + "_complete_positions.txt"
# cluster kmers to contigs
# delete try later
# try:
# with open(path_pos, 'rb') as fp:
# kmer_dict = pickle.load(fp)
# except:
# with open(path_posv2, 'rb') as fp:
# kmer_dict = pickle.load(fp)
# contig_amounts_distances = bs.cluster_kmers(kmers, kmer_dict)
# contigs_classified["A." + prediction][6] = contig_amounts_distances
#del kmer_dict
for key, value in contigs_classified.items():
number_of_contigs = value[1]
# save results
results_clustering = [[key + "," + str(statistics.median(value[0])) + "," + str(number_of_contigs), str(statistics.median(value[2])) + "," + str(round(value[3]/number_of_contigs, 2)) + "," + str(statistics.median(value[4])) + "," + str(value[6])]]
#with open(r'Results/XspecT_mini_csv/Results_Clustering.csv', 'a', newline='') as file:
#writer = csv.writer(file)
#writer.writerows(results_clustering)
value[0] = "Score Median: " + str(statistics.median(value[0]))
value[1] = "Number of Contigs: " + str(number_of_contigs)
value[2] = "Contig-Length Median: " + str(statistics.median(value[2]))
value[3] = "Repetiviness: " + str(round(value[3]/number_of_contigs, 2))
value[4] = "Bootstrap Median: " + str(statistics.median(value[4]))
#value[6] = "Clusters: " + str(value[6])
contigs_classified[key] = value
print("Species: ", key)
print(value[0])
print(value[1])
print(value[2])
print(value[3])
print(value[4])
print(value[6])
print()
# aktuelle Speichernutzung auslesen
process = psutil.Process()
memory_info = process.memory_info()
# Ausgabe des Speicherverbrauchs
print(f"Aktueller Speicherverbrauch: {memory_info.rss / 1024 / 1024:.2f} MB")
save_contigs = "none"
if save_contigs != "none":
with open(r"Results/Contigs_saved.fasta", 'w') as file:
for j in range(len(contig_header)):
file.write(contig_header[j] + "\n")
file.write(contig_seq[j] + "\n")
file.write("\n")
elif file_format == "fastq" or file_format == "fq":
if metagenome:
BF_1_1.kmer_hits_single = []
BF_1_1.number_of_kmeres = 0
BF_1_1.hits_per_filter = [0] * BF.clonetypes
counter = 0
reads = []
reads_classified = {}
reads_passed = 0
ambiguous_reads = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
BF_1_1.kmer_hits_single = []
BF_1_1.number_of_kmeres = 0
BF_1_1.hits_per_filter = [0] * BF.clonetypes
# reverse_sequence = sequence.seq.reverse_complement()
read_kmers = []
reads = []
if counter < read_amount:
counter += 1
else:
break
k1 = str(sequence.seq[0:BF_1_1.k]) # first k-mer
k2 = str(sequence.seq[len(str(sequence.seq)) - BF_1_1.k:]) # last k-mer
mid = len(str(sequence.seq)) // 2
k3 = str(sequence.seq[mid:mid + BF_1_1.k]) # k-mer in middle
k4 = str(sequence.seq[BF_1_1.k:BF_1_1.k * 2])
k5 = str(sequence.seq[mid + BF_1_1.k:mid + BF_1_1.k * 2])
# Taking sum of list as reference, if sum has not increased after testing those 3 kmeres,
# then the read won't be tested further
hit_sum = sum(BF_1_1.hits_per_filter)
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
#sample_size = int(len(str(sequence.seq)) ** 0.5)
#threshold_read = sample_size * 0.7
#for i in range(0, len(str(sequence.seq)) - BF_1_1.k, sample_size):
# if "N" not in str(sequence.seq[i: i + BF_1_1.k]):
# BF_1_1.lookup(str(sequence.seq[i: i + BF_1_1.k]))
if "N" not in str(sequence.seq):
BF_1_1.lookup(k1)
BF_1_1.lookup(k2)
BF_1_1.lookup(k3)
BF_1_1.lookup(k4)
BF_1_1.lookup(k5)
else:
continue
# needs at least 2 of 3 hits to continue with read
if (sum(BF_1_1.hits_per_filter) - hit_sum) > 3:
for j in range(len(str(sequence.seq)) - BF_1_1.k):
read_kmers.append(str(sequence.seq[j: j + BF_1_1.k]))
# read_kmers.append(str(reverse_sequence[j: j + BF_1_1.k]))
reads.append(read_kmers)
BF_1_1.hits_per_filter = hits_per_filter_copy
#print("Read passed 1. Threshold")
else:
# resetting hit counter
BF_1_1.hits_per_filter = hits_per_filter_copy
continue
#reads_filtered = set()
reads_filtered = []
#print("Read-Length: ", len(reads[0]))
for i in range(len(reads)):
threshold = 0
# hits_per_filter_copy = BF_1.hits_per_filter[:]
# read_len = len(reads[i])
# BF_1.lookup(reads[i][0])
# BF_1.lookup(reads[i][int(read_len/2)])
# BF_1.lookup(reads[i][-1])
# if (BF_1.hits_per_filter[0] - hits_per_filter_copy[0]) < 2:
# continue
for j in range(len(reads[i])):
BF_1_1.number_of_kmeres += 1
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
if "N" not in reads[i][j]:
BF_1_1.lookup(reads[i][j])
if hits_per_filter_copy != BF_1_1.hits_per_filter:
threshold += 1
if threshold >= 0.8 * len(reads[i]):
reads_filtered += reads[i]
if len(reads_filtered) == 0:
continue
#else:
#reads_passed += 1
# reads_filtered.add(reads[i][j])
# reads_filtered.append(reads[i][j])
# reads_filtered.append(list(read_kmers_filtered_unique))
#print("Kmer Anzahl: ", len(reads_filtered))
BF.number_of_kmeres = 0
BF.hits_per_filter = [0] * BF.clonetypes
BF.kmer_hits_single = []
for kmer in reads_filtered:
if "N" not in kmer:
BF.number_of_kmeres += 1
kmer_reversed = str(Seq(kmer).reverse_complement())
if kmer > kmer_reversed:
BF.lookup(kmer)
else:
BF.lookup(kmer_reversed)
else:
continue
#BF.number_of_kmeres += 1
#if ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) <= 5) and ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) != 0):
# PLatzhalter
#BF.number_of_kmeres += 1
#elif (sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) > 5:
#BF.hits_per_filter = hits_per_filter_copy[:]
# PLatzhalter
#BF.number_of_kmeres += 0
#print(BF.hits_per_filter)
score = BF.get_score()
names = []
BioMonitoring = False
if BioMonitoring:
with open(r'filter/FilterCulicidaeSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
else:
with open(r'filter/FilterSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
score_edit = [str(x) for x in score]
score_edit = ",".join(score_edit)
# making prediction
index_result = max(range(len(score)), key=score.__getitem__)
prediction = names[index_result]
if max(score) == sorted(score)[-2]:
ambiguous_reads += 1
continue
#print(prediction, max(score), sorted(score)[-2]/max(score))
#if max(score) == 0:
#continue
# Repetiviness threshold
#if sorted(score)[-2]/max(score) > 0.6:
#continue
# bootstrapping
bootstrap_n = 100
samples = bs.bootstrap(BF.kmer_hits_single, BF.number_of_kmeres, bootstrap_n)
sample_scores = bs.bootstrap_scores(samples, BF.number_of_kmeres, BF.clonetypes)
bootstrap_score = 0
bootstrap_predictions = []
for i in range(len(sample_scores)):
if max(sample_scores[i]) != sorted(sample_scores[i])[-2]:
bootstrap_predictions.append(names[max(range(len(sample_scores[i])), key=sample_scores[i].__getitem__)])
if max(range(len(sample_scores[i])), key=sample_scores[i].__getitem__) == index_result:
bootstrap_score += 1
else:
continue
bootstrap_score = bootstrap_score/bootstrap_n
#count = Counter(bootstrap_predictions)
#max_element = max(count, key=count.get)
#bootstrap_result = [max_element, count[max_element]]
#controle = array(BF.kmer_hits_single)
#temp = sum(controle, 0)
#controle_score = []
#for j in range(BF.clonetypes):
# if temp[j] == 0:
# controle_score.append(0.0)
#else:
#controle_score.append(round(float(temp[j]) / float(BF.number_of_kmeres), 2))
#print(len(BF.kmer_hits_single))
#print(temp)
#print("Controle Score: ",controle_score)
#print("OG Score: ",score)
#print(bootstrap_score)
#if bootstrap_score < 0.3:
#print(score)
#print("Original prediction: ",prediction, " BT result: ", bootstrap_result)
#if prediction != "baumannii" and max(score) >= 0.3:
#print(BF.hits_per_filter)
#print(prediction, sorted(score)[-2]/max(score))
#if sorted(score)[-2]/max(score) >= 0.6:
#continue
#elif prediction == "baumannii":
#if sorted(score)[-2]/max(score) >= 0.6:
#continue
if False:
#if max(score) < 0.3:
if "unkown" not in reads_classified:
reads_classified["unknown"] = [1]
else:
reads_classified["unknown"][0] += 1
#scores.append(str(max(score)))
else:
if BioMonitoring:
predictions.append(prediction)
scores.append(str(max(score)))
else:
if ("A." + prediction) not in reads_classified:
reads_classified["A." + prediction] = [max(score), 1, sorted(score)[-2]/max(score), BF.number_of_kmeres, [bootstrap_score], reads_filtered, None]
else:
reads_classified["A." + prediction][1] += 1
reads_classified["A." + prediction][0] += max(score)
reads_classified["A." + prediction][2] += sorted(score)[-2]/max(score)
reads_classified["A." + prediction][3] += BF.number_of_kmeres
reads_classified["A." + prediction][4] += [bootstrap_score]
reads_classified["A." + prediction][5] += reads_filtered
# for i in range(len(reads_filtered)):
# hits_per_filter_copy = BF.hits_per_filter[:]
# read_len = len(reads_filtered[i])
# BF.lookup(reads[i][0])
# BF.lookup(reads[i][int(read_len/2)])
# BF.lookup(reads[i][-1])
# for j in range(len(reads_filtered[i])):
# hits_per_filter_copy = BF.hits_per_filter[:]
# BF.lookup(reads_filtered[i][j])
# if (sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) == 0:
# continue
# if ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) <= 5) and ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) != 0):
# BF.number_of_kmeres += 1
# if BF.hits_per_filter[50] != hits_per_filter_copy[50]:
# print(reads_filtered[i][j])
# elif (sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) > 5:
# BF.hits_per_filter = hits_per_filter_copy[:]
# for sequence in SeqIO.parse(paths[i], "fastq"):
# if counter < read_amount:
# counter += 1
# for j in range(0, len(sequence.seq) - BF.k+1, 10):
# BF.number_of_kmeres += 1
# BF.lookup(str(sequence.seq[j: j + BF.k]))
# else:
# break
# counter = 0
# for sequence in SeqIO.parse(paths[i], "fastq"):
# reverse_sequence = sequence.seq.reverse_complement()
# if counter < read_amount:
# counter += 1
# for j in range(0, len(reverse_sequence) - BF.k+1, 10):
# BF.number_of_kmeres += 1
# BF.lookup(str(reverse_sequence[j: j + BF.k]))
# else:
# break
else:
counter = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
if counter < read_amount:
counter += 1
for j in range(0, len(sequence.seq) - BF.k+1, 10):
BF.number_of_kmeres += 1
kmer = str(sequence.seq[j : j + BF.k])
kmer_reversed = str(Seq(kmer).reverse_complement())
if kmer > kmer_reversed:
BF.lookup(kmer)
else:
BF.lookup(kmer_reversed)
else:
break
score = BF.get_score()
if metagenome:
#for prediction in reads_classified:
# kmers = reads_classified[prediction][5]
# Strip "A."
# prediction = prediction[2:]
# kmer mapping to genome, start by loading the kmer_dict in
# path_pos = "filter\kmer_positions\Acinetobacter\\" + prediction + "_positions.txt"
# delete later
# path_posv2 = "filter\kmer_positions\Acinetobacter\\" + prediction + "_complete_positions.txt"
# cluster kmers to contigs
# delete try later
# start_dict = time.time()
# try:
# with open(path_pos, 'rb') as fp:
# kmer_dict = pickle.load(fp)
# except:
# with open(path_posv2, 'rb') as fp:
# kmer_dict = pickle.load(fp)
# end_dict = time.time()
# needed_dict = round(end_dict - start_dict, 2)
# print("Time needed to load kmer_dict in: ", needed_dict)
# contig_amounts_distances = bs.cluster_kmers(kmers, kmer_dict)
# reads_classified["A." + prediction][6] = contig_amounts_distances
for key, value in reads_classified.items():
if key == "unknown":
continue
value.insert(2, value[0]/value[1])
value.pop(0)
reads_classified[key] = value
print(key, value[0], round(value[1], 2), round(value[2]/value[0], 2), round(value[3]/value[0], 2), statistics.median(value[4]), value[6])
names = []
BioMonitoring = False
if BioMonitoring:
with open(r'filter/FilterCulicidaeSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
else:
with open(r'filter/FilterSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
score_edit = [str(x) for x in score]
score_edit = ",".join(score_edit)
# making prediction
BioMonitoring = False
if not metagenome and not BioMonitoring:
prediction = Classifier.classify(r'Training_data/Training_data_spec.csv', score, True)
else:
index_result = max(range(len(score)), key=score.__getitem__)
prediction = names[index_result]
if False:
predictions.append("unknown")
scores.append(str(max(score)))
else:
if BioMonitoring:
predictions.append(prediction)
scores.append(str(max(score)))
else:
predictions.append("A. " + prediction)
scores.append(str(max(score)))
if BioMonitoring:
for i in range(len(predictions)):
print(files[i] + ": " + "Predicted species: ", predictions[i], " Score: ", scores[i])
print("Taxonomic assignment done...")
return predictions, scores
def clAssT(BF_2, files, paths, file_format, read_amount):
print("Starting strain-typing on sub-type-level...")
predictions_ClAssT = []
scores_ClAssT = []
for i in range(len(files)):
if i == int(len(files)/6) or i == int(len(files)/3) or i == int(len(files)/2) or i == int(len(files)/1.5) or i == int(len(files)/1.2):
print("...")
BF_2.number_of_kmeres = 0
BF_2.hits_per_filter = [0] * BF_2.clonetypes
if file_format == "fasta" or file_format == "fna":
for sequence in SeqIO.parse(paths[i], "fasta"):
# Originally 10
for j in range(0, len(sequence.seq) - BF_2.k, 500):
BF_2.number_of_kmeres += 1
BF_2.lookup(str(sequence.seq[j: j + BF_2.k]))
elif file_format == "fastq" or file_format == "fq":
counter = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
if counter < read_amount:
counter += 1
for j in range(0, len(sequence.seq) - BF_2.k+1, 10):
BF_2.number_of_kmeres += 1
BF_2.lookup(str(sequence.seq[j: j + BF_2.k]))
else:
break
score_ClAssT = BF_2.get_score()
score_edit_ClAssT = [str(x) for x in score_ClAssT]
score_edit_ClAssT = ",".join(score_edit_ClAssT)
prediction_ClAssT = Classifier.classify(r'Training_data/Training_data_IC.csv', score_ClAssT, [True,True,True,True,True,True,True,True,False])
predictions_ClAssT.append(prediction_ClAssT)
scores_ClAssT.append(str(max(score_ClAssT)))
print("Strain-typing on sub-type-level done...")
return predictions_ClAssT, scores_ClAssT
def blaOXA(BF_3, files, paths, file_format, read_amount):
start = time.time()
print("Start screening for blaOXA-genes...")
paths_oxa = sorted(os.listdir(r"filter/OXAs/families"))
BF_families = BF_3["OXA-families"]
oxas = []
scores_oxa = []
scores_oxa_ind = []
for i in paths_oxa:
oxas.append(i[:-4])
#print("OXA-families: ", oxas) # correct
for i in range(len(files)):
oxa_dic = {}
if i == int(len(files)/6) or i == int(len(files)/3) or i == int(len(files)/2) or i == int(len(files)/1.5) or i == int(len(files)/1.2):
print("...")
# Checking file type
# if the file is fasta -> concat lines
reads = []
BF_families.number_of_kmeres = 0
BF_families.hits_per_filter = [0] * BF_families.clonetypes
BF_families.table = OXATable()
BF_families.table.read_dic(r'filter/OXAs_dict/oxa_dict.txt')
if file_format == "fasta" or file_format == "fna":
for sequence in SeqIO.parse(paths[i], "fasta"):
reads.append(str(sequence.seq))
BF_families.lookup_oxa(reads, ".fna")
elif file_format == "fastq" or file_format == "fq":
counter = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
if counter < read_amount:
counter += 1
reads.append(str(sequence.seq))
else:
break
BF_families.lookup_oxa(reads, ".fq")
#print("Reads used: ", counter)
score_oxa = BF_families.get_oxa_score()
#print("Score: ", score_oxa)
for i in range(len(oxas)):
oxa_dic[oxas[i]] = score_oxa[i]
for i in range(len(oxa_dic)):
if oxa_dic[oxas[i]] < 0.3:
del oxa_dic[oxas[i]]
if len(oxa_dic) == 0:
oxa_dic = "None"
if oxa_dic != "None":
oxa_dic = dict(sorted(oxa_dic.items(), key=lambda item: item[1]))
scores_oxa.append(oxa_dic)
# prepare data for next taxonomic level
oxa_names = []
#print(oxa_dic)
for oxa_family in oxa_dic:
oxa_names.append(oxa_family[:-7])
for oxa_family in oxa_names:
if oxa_dic == "None":
scores_oxa_ind.append(["None", 0])
break
#print("blaOXA: ", oxa_dic)
oxa_dic_ind = {}
## TODO:
#print("blaOXA: ", oxa_family)
BF_ind = BF_3[oxa_family]
BF_ind.number_of_kmeres = 0
BF_ind.hits_per_filter = [0] * BF_ind.clonetypes
BF_ind.table = OXATable()
BF_ind.table.read_dic(r'filter/OXAs_dict/oxa_dict.txt')
paths_oxa = sorted(os.listdir(r"filter/OXAs/individual/" + oxa_family))
oxas_ind = []
for i in paths_oxa:
oxas_ind.append(i[:-4])
if file_format == "fasta" or file_format == "fna":
BF_ind.lookup_oxa(reads, ".fna")
elif file_format == "fastq" or file_format == "fq":
BF_ind.lookup_oxa(reads, ".fq")
score_oxa = BF_ind.get_oxa_score()
# build dict with oxa-gen and its score
for i in range(len(oxas_ind)):
oxa_dic_ind[oxas_ind[i]] = score_oxa[i]
# filter dict by score
if len(oxa_dic_ind) == 0 or max(oxa_dic_ind.values()) < 0.3:
scores_oxa_ind.append("None")
else:
scores_oxa_ind.append([max(oxa_dic_ind, key=oxa_dic_ind.get), oxa_dic_ind[max(oxa_dic_ind, key=oxa_dic_ind.get)]])
end = time.time()
needed = round(end - start, 2)
print("Time needed: ", needed)
print("Screening for blaOXA-genes done...")
return scores_oxa, scores_oxa_ind